Navigation systems detect and track positions of patients, parts of the patient's body, regions to be treated, as well as positions of treatment devices. This information (e.g., images, numerical data, textual data, etc.) then may be displayed on a monitor or the like, and a surgeon may use the images to support treatment. In order to correctly and accurately show the images to the surgeon, the images are correlated with the actual patient space, and this process is called registration.
The idea of feature or object based matching based on, say, fiducials or anatomical landmarks to register a patient can be found in nearly all image guided surgery products. These methods basically utilize ‘easy-to-identify’ structures in the image set of the patient (usually MR or CT based tomography images) and ‘easy-to-reach’ structures rigidly attached to the patient. These structures enable the image set space to be correlated with the actual patient orientation in the operating room.
‘Easy-to-identify’ structures in image sets are usually, but not restricted to, spherical markers or rods that provide high image contrast. The markers typically are attached to the patient in such a way that they are also ‘easy-to-reach’ with a tracked instrument of the image guided surgery (IGS) system. The quality of the registration, however, depends on the expertise of the person (e.g., his expertise in applying the right number of markers or rods in the right configuration, and the individual setup of the patient bedding, which may limit access to markers or rods). It is not always guaranteed that structures are ‘easy-to-identify’, because they can be ambiguous, and corresponding structures are not always ‘easy-to-reach’, because of the patient bedding and specific operating room setup, e.g., drapes, tubes, etc. Hence, consistent registration quality using conventional feature or object based matching, especially when based on fiducials such as registration markers, is not guaranteed.
Such manual registration methods, in comparison to automatic registration methods, usually require a significant amount of time to attach and identify the registration markers and, therefore, such methods may not be feasible for intraoperative use. On the other hand, a disadvantage of automatic registration methods is that they are proprietary and require strong integration in the navigation software.
In contrast, standard paired-point matching image registration, one embodiment of feature or object based matching, is available with typically any commercially available IGS system. In this case, however, accuracy might be low and one needs to access the patient anatomy for image registration. This is typically not possible for the registration of intra-operatively acquired images, because the patient anatomy is not accessible due to draping, etc.
The registration of intraoperative images directly affects navigation systems. For example, problems may arise in the course of treatment if, during treatment, the tissue is subjected to shifting, as may happen, for example, due to liquid discharge or removal of tissue. In such a situation (e.g., if the target of treatment or the surrounding tissue together with the target of treatment has been shifted), the supporting navigation may become inaccurate. As a result, the surgeon can only rely on his own observations or, if he did not notice the shift, he may operate at wrong positions.
A method for supporting the treatment of a patient is described in US 2001/0007918 A1.